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Protein structure prediction with a focus on Rosetta
This presentation discusses protein structure prediction using Rosetta. It begins with an overview of the Critical Assessment of Protein Structure Prediction (CASP) experiments and notes that Rosetta is one of the top performing free-modeling servers. The presentation then describes the basic ab initio protocol used by Rosetta, which involves fragment insertion, scoring, and refinement. It also discusses limitations and success rates. Key aspects of the Rosetta energy functions and sampling algorithms are presented. Examples of specific Rosetta applications including low-resolution modeling and refinement are provided.
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This section introduces Rosetta and its focus on protein structure prediction, outlining training sessions and computational biology activities.
Discusses the ab initio prediction method and CASP, emphasizing the evaluation of protein structure prediction techniques.
Explains Rosetta's versatile applications in macromolecular modeling, structures prediction, and associated success rates. Details on the specific functionalities of Rosetta, including structure prediction and refinement techniques for various protein domains.
Explores folding theories, the role of sequence-structure relationships, and the creation of fragment libraries for modeling.
Discusses different energy functions used in Rosetta for low-resolution and high-resolution structure predictions.
Describes the step-by-step computational processes and protocols employed in Rosetta to enhance protein folding simulations.
Details the use of constraints in protein structure predictions and specialized techniques for membrane protein modeling.
Analyzes molecular dynamics simulations' role in folding studies, including rates and advancements in high-performance computations.
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Protein structure prediction with a focus on Rosetta
- 1. 1 WITH A FOCUSON ROSETTA This presentation was prepared by: Xavier Ambroggio, ambroggiox@niaid.nih.gov PROTEIN STRUCTURE PREDICTION OFFICE OF CYBER INFRASTRUCTURE AND COMPUTATIONAL BIOLOGY NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
- 2. Fall 2011 ComputationalStructural Biology Seminar Series 2 9 – 11 AM, T/Th in 12A/B51 http://training.cit.nih.gov Week Day Date Course Instructor CIT Course # Week 1 Tues Aug. 23 Fundamentals, Data Sources, and Visualization of Macromolecular Structure Darrell Hurt SS260-11001 Thurs Aug. 25 Generating Protein Structures from Homology Darrell Hurt SS270-11001 Week 2 Tues Aug. 30 Predicting Protein Structures from Amino Acid Sequences Xavier Ambroggio SS660-11001 Thurs Sept. 1 Predicting Macromolecular Complexes from Uncomplexed Structures Xavier Ambroggio SS670-11001 Week 3 Tues Sept. 6 Design and Analysis of Macromolecular Interfaces Xavier Ambroggio SS770-11001 Thurs Sept. 8 Analysis and Advanced Visualization of Macromolecular Structure Darrell Hurt SS330-11001 Week 4 Tues Sept. 13 Computational Drug Design Mike Dolan SS340-11001 Thurs Sept. 15 Introduction to Molecular Dynamics Mike Dolan TBA Week 5 Thurs. Sept. 22 Advanced Molecular Dynamics Mike Dolan TBA
- 3. Bioinformatics and ComputationalBiosciences Branch 3 Scientific Collaboration Scientific Training Custom Scientific Software & Infrastructure • Structural Biology • Phylogenetics • Statistics • Sequence Analysis • Microarray Analysis • NGS Analysis • Bioinformatics • Biological Networks • Function Prediction • …
- 4. 4 Ab Initio StructurePrediction: Given an amino acid sequence, find the tertiary structure “Protein folding problem”
- 5. CASP: Critical Assessmentof protein Structure Prediction http://predictioncenter.org • Double-blind experiment (…competition) • World-wide scientific community • Unbiased assessment of techniques in structure prediction • Biennial (every even year) • “Pulse” of the prediction community • What can be predicted? • Which servers/algorithms perform best?
- 6.
- 7. CASP Top Free-ModelingServers 7 Why Rosetta focus? • Standalone • Versatile RNA design dock … • Open Source • Substantial Literature • Shared methodology Use any and all available servers!!!
- 8. Das & BakerAnnu. Rev. Biochem 2008 prediction design Rosetta: multipurpose macromolecular modeling suite CIT Course # SS660-11001 CIT Course # SS670-11001 CIT Course # SS770-11001
- 9. ab initio predictthe structure from sequence relax refine the structure using Rosetta energy functions idealize replace bond geometries with ideal values loop modeling build and refine local structurally variable regions in context of a structural template design optimize sequence given a structure with a fixed backbone docking structure prediction for a protein-protein complex given subunits ligand ligand docking ddG prediction protein-protein interface and protein stability ddG stability calculations for mutations scoring score input conformations with Rosetta energy functions RNA predict RNA structures from sequences and design sequences from fixed structures clustering grouping input structures by RMSD to each other for structure prediction analysis backrub generate alternate backbone conformations based on sets of rotations membrane ab initio predict the structures of helical membrane proteins enzyme design redesign a protein around a ligand domain assembly fixed domains connected by variable regions antibody automated antibody homology modeling XML parsing Parse XML scripts into protocols Brief Description of Select Rosetta Functions
- 10. What types ofprotein domains can Rosetta fold? Small, globular, soluble protein domains… Small, simple membrane protein domains… …but not complex domains or multi-domain proteins. T4-lysozyme C-terminal domain V-type Na+ ATP synthase subunit rhodopsin Slide content adapted from Stephanie Hirst at the 2011 Vanderbilt Rosetta Workshop A B C
- 11. What are thesuccess rates? High resolution predictions are achievable • targets ≤100 residues • success rate ~30% • success rate with accurate secondary structure ~50% • a hallmark of accuracy: convergence 11 Slide content courtesy Rhiju Das, Baker Lab
- 12. What types ofprotein domains can no one fold? CASP9: domains with no good FM predictions Slide content adapted from talk given by Lisa Kinch of the Grishin lab at CASP9 mee>ng: h@p://predic>oncenter.org/casp9/ • Non-‐globular • Trimeric • Fe stabilized • High contact order Many residues close in 3D, far in 1D • + elongated sheet? T0591d1, 3MWT T0550d2, 3NQK T0629d2, 2XGF
- 13. 1. Select fragments consistent with local sequence preferences 2. Assemble fragments into models with na>ve-‐like global proper>es 3. Iden>fy the best model from the popula>on of decoys Slide content adapted from Ora Schueler-Furman’s “Workshop in Structural Computational Biology” Figures adapted from Charlie Strauss; Protein structure prediction using ROSETTA, Rohl et al (2004) Methods in Enzymology, 383:66 Basic Ab Ini'o Rose<a protocol
- 14. Assembly Decoy Decoy Decoy Decoy Decoy Decoy Decoy Decoy Decoy Fragment Fragment Fragment Fragment Fragment Fragment Fragment Fragment Fragment Fragment Decoy Fragment-Based Structure Prediction Rosetta, Quark, … Template(s) Template(s) Template(s) Template(s) Template(s) Template(s) Template(s) Template(s) Template(s) Template(s) Template(s) Model Alignment Homology modeling:
- 15. First atomic-resolution model Target0281 CASP6 • Topology sampled by ab initio trajectory of homolog sequence (rmsd=2.2Å) • Full atom refinement reduces rmsd to 1.5Å • Side chain packing accurately recovered Slide content adapted from Ora Schueler-Furman’s “Workshop in Structural Computational Biology” Figures adapted from Bradley P, Malmström L, Qian B, Schonbrun J, Chivian D, Kim DE, Meiler J, Misura KM, Baker D. Free modeling with Rosetta in CASP6. Proteins.
- 16. Folding Theory: Sequence-StructureRelationships 16 • Secondary structure formation is the earliest part of the folding process • Local sequence codes for local structures… i.e. fragments helical sequences in a folded protein tend to be helical in isolation • Secondary structure prediction algorithms have ~70-80% accuracy Partial failure due to tertiary interactions stabilizing secondary structure elements
- 17. Rosetta fragments • 3and 9 residue fragments matched to query sequence • database created from crystal structures < 2.5Å resolution < 50% sequence identity • low resolution modeling centroid representation of side chains • ranked by: alignment Secondary structure predictions • PSI-PRED • SAM-T02 • Jufo • PhD 17
- 18. KVFGRCELAAAMKRHGLDNYRGYSLGNWVC... KVF KVFGRCELA VFG VFGRCELAA FGR FGRCELAAA GRC GRCELAAAM --------------------------------- EEEE TT SEEEEEEE TT HH... query sec str Slide content courtesy David Hoover, CIT, NIH Sliding fragment windows
- 19. # Rank GK L M Q E R A 13 1000 G K L 25 821 G R L 46 1000 K L M 21 635 R L M 43 923 K V M 26 523 R V M 15 970 M Q E 26 934 E R A Separate 3-mer and 9-mer libraries generated Slide content courtesy David Hoover, CIT, NIH Example 3-mer fragment library
- 20. Making Fragment Librarieswith Robetta http://robetta.bakerlab.org/ Slide content adapted from Stephanie Hirst at the 2011 Vanderbilt Rosetta Workshop
- 21. Making Fragment Librarieson Biowulf Slide content by David Hoover from: http://biowulf.nih.gov/apps/Rosetta23.html#RosettaFragments
- 22. 22 • Levinthal paradox: Given either alpha, beta, or loop conformation, for protein of nres, 3nres possible conformations. If nres = 100, sampling a conformation every 10-13 seconds = 1027 years to fold Universe is 1010 years old. Folding is non-random and cooperative. • Many different combinations of secondary structure elements have similar stabilities Tertiary (side-chain level) interactions drive folding towards the native topology Phase transition results in a substantial energy gap between native and non-native structures Folding Theory: The Folding Landscape • Cyrus Levinthal, J. Chim. Phys. 65, 44; 1968 • Hue Sun Chan and Ken A. Dill, Protein Folding in the Landscape Perspective: Chevron Plots and Non- Arrhenius Kinetics, Proteins: Structure, Function, and Genetics, Volume 30, No. 1, January 1998, pp 2-33. Implications and requirements for folding algorithm: • Fast conformational sampling algorithm • Accurate scoring function • Full-atom modeling
- 23. early centroid modelscentroid models final full-atom models Assembly Coarse funnel to native-like decoys Fine-grained funnel to near-native decoys
- 24. Major Classes ofEnergy Functions in Rosetta 24 Low resolution: reduced atom representation (centroid) simplified energy function used for aggressive search of state space High resolution: full-atom representation detailed energy function local search of state space refinement and minimization General weighted sum of linear terms: Energy = w1*term1 + w2*term2 + … pairwise decomposable (speed) weighted for task, e.g. ligand docking
- 25. Low resolution (centroid)folding 25 Fragment insertion conformation modification occurs in torsion space initial insertions result in large changes in dihedrals 9 mers inserted first followed by 3 mers later in process later insertions purposefully result in small changes in dihedrals random insertion * *
- 26. Sss + SHS- sheet and helix-sheet geometries • Scβ density/compactness of structure • Svdw no clashes • SRgyr radius of gyra>on (Rgyr), globular structure Slide content adapted from Ora Schueler-Furman’s “Workshop in Structural Computational Biology” Driving assembly towards native-like decoys
- 27. Low-resolution homolog foldingimproves prediction • Collect homologs • Create low-resolution models cluster • Thread query sequence onto models • Proceed to fullatom refinement … … … Slide content adapted from Ora Schueler-Furman’s “Workshop in Structural Computational Biology”
- 28. Low resolution (centroid)folding example 28
- 29.
- 30. 30 High resolution (full-atom)refinement Chen Y et al. Nucl. Acids Res. 2004;32:5147-5162 evaluating/optimizing specific atom-atom interactions e.g. hydrogen bonding:
- 31. Comparison of lowresolution, relax, and abrelax folding example 31
- 32. 32 Examples from theRosetta@home archive of top predictions Note: massively parallel computation rosetta prediction crystal structure
- 33. Detailed ab initioRosetta Workflow 33 INPUT • amino acid sequence • secondary structure prediction(s) • fragment library • constraints from experimental data • NMR • biochemical/biophysical studies • ... LOW RESOLUTION FOLDING • fragment insertions • scoring • filters CLUSTERING • groups of decoys with low RMSD to each other • lowest energy decoy of clusters selected for further refinement or prediction HIGH RESOLUTION REFINEMENT • backbone minimization • rotamer optimization ADDITIONAL MODELING • identifying variable regions • rebuilding >103-106 trajectories automated manual
- 34. 34 Computational Considerations Protocol UtilityCaveats Centroid • fast • widely sample conformational space • possibility of no near-native models after low resolution folding • no discrimination by energy Full-atom refinement • near-native decoys separated by energy • more computationally demanding • must have near-native in starting decoy pool Combined • streamlined • for powerful and massively parallel computing • most computationally demanding • improvement only with sufficient sampling
- 35. 35 Native (CheY) A ~1000-foldincrease in computational power Slide content courtesy Rhiju Das, Baker Lab
- 36. 36 Architect of Rosetta@home:David Kim A ~1000-fold increase in computational power Native (CheY) Lowest energy Rosetta structure “brute force” approach
- 37. Computational power vs.accuracy in ab initio structure prediction 37 Cα RMSD of lowest energy model to the native structure vs. sample size Sample Size RMSDtonative Category 1: Successful high-resolution predictions Category 2: Successful high-resolution predictions with additional sampling Category 3: Unsuccessful predictions (with any amount of sampling) Kim DE, Blum B, Bradley P, Baker D. Sampling bottlenecks in de novo protein structure prediction. J Mol Biol. 2009 Oct 16;393(1):249-60.
- 38. 38 “De novo” phasing:large-scale tests Tests on 30 data sets (covering 16 proteins) Slide content courtesy Rhiju Das, Baker Lab; Bin et al., Nature 2007. TF Z-score Have I solved it? < 5 no 5 - 6 unlikely 6 - 7 possibly 7 - 8 probably > 8 definitely
- 39. 39 “De novo” phasing:large-scale tests Tests on 30 data sets (covering 16 proteins) 1hz5-sf.cif Å Slide content courtesy Rhiju Das, Baker Lab; Bin et al., Nature 2007. Rosetta-refined native (positive controls) Rosetta-refined de novo models
- 40. 40 “De novo” phasing:large-scale tests Tests on 30 data sets (covering 16 proteins) 1hz5-sf.cif Success in 14/30 data sets Å Slide content courtesy Rhiju Das, Baker Lab; Bin et al., Nature 2007. Rosetta-refined native (positive controls) Rosetta-refined de novo models
- 41. 41 “De novo” phasing:large-scale tests Tests on 30 data sets (covering 16 proteins) Rosetta-refined native (positive controls) Rosetta-refined de novo models Rosetta-refined de novo models, fragments with correct native 2° structure 1hz5-sf.cif Å Slide content courtesy Rhiju Das, Baker Lab; Bin et al., Nature 2007.
- 42. Preparation for foldingsimulations • proper secondary structure assignment • constraints • limit search space • increase sampling efficiency • decrease CPU time 42
- 43. Constraints • There areconstraint types and function types Constraint types: AtomPair, Angle, Dihedral, etc. Function types: Bounded, Spline, Harmonic, Gaussian, etc. • Each constraint is scored individually and the total constraint score is the sum of all individual scores • Each constraint can have its own constraint type and function type. In some cases, like when using Spline function, each constraint can have its own weight • How you define the constraint and how it’s scored depends on the constraint type; this is same with function type. Slide content adapted from Stephanie Hirst at the 2011 Vanderbilt Rosetta Workshop
- 44. Constraint file example:EPR data <cst type> <atom1> <res1> <atom2> <res2> <cst_func> <RosettaEPR> <Dcb> <weight> <bin>! AtomPair CB 32 CB 36 SPLINE EPR_DISTANCE 16.0 1.0 0.5! AtomPair CB 59 CB 74 SPLINE EPR_DISTANCE 19.0 1.0 0.5! AtomPair CB 62 CB 71 SPLINE EPR_DISTANCE 19.0 1.0 0.5! AtomPair CB 62 CB 74 SPLINE EPR_DISTANCE 25.0 1.0 0.5! AtomPair CB 63 CB 74 SPLINE EPR_DISTANCE 14.0 1.0 0.5! AtomPair CB 66 CB 74 SPLINE EPR_DISTANCE 23.0 1.0 0.5! AtomPair CB 83 CB 90 SPLINE EPR_DISTANCE 13.0 1.0 0.5! Constraint info Constraint Function info Slide content adapted from Stephanie Hirst at the 2011 Vanderbilt Rosetta Workshop
- 45. Membrane protein abinitio • RosettaMembrane divides the protein into: hydrophobic hydrophilic soluble layers • Specific scoring function for each layer Slide content adapted from Stephanie Hirst at the 2011 Vanderbilt Rosetta Workshop Figure from Yarov-Yarovoy, Schonbrun, and Baker 2006.
- 46. Input Files Spanfile -‐ *.span -‐-‐transmembrane topology predic>on file generated using octopus2span.pl script -‐-‐Input OCTOPUS topology file is generated at h@p://octopus.cbr.su.se using protein sequence as input. Lipopholicity predicDon file -‐ *.lips4 -‐-‐Generate using run_lips.pl script -‐-‐Need input FASTA file, spanfile, blaspgp and nr (NCBI) database to run Fragment generaDon -‐-‐Advised to use SAM but not JUFO or PSIPRED, which predict TMH regions poorly Slide content adapted from Stephanie Hirst at the 2011 Vanderbilt Rosetta Workshop
- 47. Folding and studyingfolding with molecular dynamics Specialized hardware, ANTON capable of continuous ms length trajectories Standard simulations: 1 - 3 µs simulations ~ months of HPC Approximate Rates of Folding: 1 µs helix 10 µs sheet 100 µs fast folding protein 1+ ms typical protein
- 48. D E Shawet al. Science 2010;330:341-346 simulation of villin at 300 K 2-8 µs folder simulation of FiP35 at 337 K 20-80 µs folder Blue: x-ray structures Red: last frame of MD simulation Folding proteins at x-ray resolution
- 49. Published by AAAS tipof hairpin 1 (12-18, blue) hairpin 1 (8-22, green) hairpin 2 (19-30, orange) full protein (2-33, red) D E Shaw et al. Science 2010;330:341-346 Reversible folding simulation of FiP35.
- 50. Thank You For questionsor comments please contact: ScienceApps@niaid.nih.gov 301.496.4455 50